Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

ABSTRACT Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model

Gene flow, through allele migration and spread, is critical in determining patterns of population genetic structure, divergence and local adaptation. While evolutionary theory has typically envisioned gene flow as a continuous connection among populations, many processes can render it fluctuating and intermittent. We analyze mathematically a stochastic mainland-island model in continuous time, in which migration occur as recurrent ''pulses''. We derive simple analytical approximations regarding how migration pulsedness affects the effective migration rates across a range of selection and dominance scenarios. Predictions are validated with stochastic simulations and summarized with graphical interpretations in terms of fixation probabilities. We show that migration pulsedness can decrease or increase gene flow, respectively above or below a selection threshold that is s~-1/N for additive alleles and lower for recessive deleterious alleles. We propose that pulsedness may leave a genomic detectable signature, by differentially affecting the fixation rates of loci subjected to different selection regimes. The additional migration created by pulsedness is called a ''pulsedness'' load. Our results indicate that migration pulsedness, and more broadly temporally variable migration, is important to consider for evolutionary and population genetics predictions. Specifically, it would overall be detrimental to the local adaptation and persistence of small peripheral populations.

Distributed Compressed Sensing Based Channel Parameter Estimation for MIMO-FBMC Communications

For the sparse correlation between channels in multiple input multiple output filter bank multicarrier with offset quadrature amplitude modulation (MIMO-FBMC/OQAM) systems, the distributed compressed sensing (DCS)-based channel estimation approach is studied. A sparse adaptive distributed sparse channel estimation method based on weak selection threshold is proposed. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and channel estimation is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher channel estimation performance than classical orthogonal matching pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

Abstract 041: Cardiovascular Disease, Hypertension, Diabetes And Cystatin C Jointly Predict Covid-19 Mortality Alongside Age, Male Sex And Black Ethnicity

Introduction: Studies of risk factors for severe/fatal COVID-19 to date may not have identified the optimal set of informative predictors. Hypothesis: Use of penalized regression with stability analysis may identify new, sparse sets of risk factors jointly associated with COVID-19 mortality. Methods: We investigated demographic, social, lifestyle, biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and air pollution data from UK Biobank (N=473,574) in relation to linked COVID-19 mortality, and compared with non-COVID-19 mortality. We used penalized regression models (LASSO) with stability analysis (80% selection threshold from 1,000 models with 80% subsampling) to identify a sparse set of variables associated with COVID-19 mortality. Results: Among 43 variables considered by LASSO stability selection, cardiovascular disease, hypertension, diabetes, cystatin C, age, male sex and Black ethnicity were jointly predictive of COVID-19 mortality risk at 80% selection threshold (Figure). Of these, Black ethnicity and hypertension contributed to COVID-19 but not non-COVID-19 mortality. Conclusions: Use of LASSO stability selection identified a sparse set of predictors for COVID-19 mortality including cardiovascular disease, hypertension, diabetes and cystatin C, a marker of renal function that has also been implicated in atherogenesis and inflammation. These results indicate the importance of cardiometabolic comorbidities as predisposing factors for COVID-19 mortality. Hypertension was differentially highly selected for risk of COVID-19 mortality, suggesting the need for continued vigilance with good blood pressure control during the pandemic.

IMPROVEMENT OF FABRIC DETECTION ALGORITHM BASED ON SOBEL OPERATOR

The traditional Sobel operator has incomplete edge detection, and improper selection threshold causes edge judgment error. In this paper, non-maximum suppression combined with adaptive threshold selection is proposed for fabric defect detection. This method uses bilateral filtering for image preprocessing to eliminate the influence of noise and illumination imbalance on the image. Increase by 45 per cent。and 135。gradient calculation in two directions, using non-maximum suppression algorithm to refine the image edge, and reduce the misjudgment of edge points by adaptive threshold selection.

Probability characteristics and sensitivity of wideband noise-like signal detector with duration selectivity

Для широкополосного обнаружителя шумового сигнала с квадратором и селекторами сигнала по уровню и длительности разработана методика определения порогов селекции по уровню и длительности, если задана вероятность ложной тревоги на максимально возможном интервале ожидания сигнала. В основе методики – применение «выбросовой» вероятностной модели работы обнаружителя в режиме ожидания и использование многомерной функции распределения вероятностей нормализованного помехового индикаторного процесса. Получена вероятностная характеристика обнаружения и изложена методика оценки чувствительности двухселекторного обнаружителя. Показано, что применение селекции по длительности позволяет повысить чувствительность двухселекторного обнаружителя по сравнению с односелекторным при равной вероятности ложной тревоги. Показано, что существует оптимальное сочетание порогов селекции по уровню и длительности, когда критерием оптимальности является максимум чувствительности обнаружителя. Этот максимум достигается, когда порог селекции по длительности составляет 0.1 от периода средней квадратичной частоты флуктуаций помехового индикаторного процесса. This article proposes a method for determining the level and duration thresholds in a wideband noise-like signal detector if the probability of a false alarm is set at the maximum possible signal waiting interval. The method is based on the use of «emissional» probabilistic model of the detector in standby mode and the use of a multidimensional probability distribution function of the normalized noise indicator process. A probabilistic characteristic of detection is obtained and also described a technique for the estimation of sensitivity of a two-step selection detector. It is shown that the use of selection by duration makes possible to increase the sensitivity of a two-step selection detector in comparison with a single-step selection detector with an equal probability of a false alarm. It is shown that there is an optimal combination of selection thresholds in terms of level and duration, when the optimality criterion is the maximum sensitivity of the detector. This maximum is reached when the duration selection threshold is about 0.1 of the period of the root-mean-square fluctuations frequency of the noise indicator process.

Determination of level and time thresholds for detectors with normalized indicator process

«Выбросовая» вероятностная модель работы обнаружителя в режиме ожидания сигнала, предложенная авторами в [1], использована для оценки влияния селекции выбросов по длительности на вероятность ложной тревоги. Флюктуационные выбросы помехового индикаторного процесса, превысившие пороги селекции по уровню и длительности, трактуются как редкие события на интервале ожидания сигнала, подчиняющиеся вероятностному закону Пуассона. При условии, что средний период следования ложных выбросов превышает интервал корреляции индикаторного процесса, получено соотношение между средним числом выбросов любой длительности и средним числом выбросов, превысивших пороговую длительность. На основании известных числовых и вероятностных характеристик выбросов нормального стационарного случайного процесса получен уравнения, связывающие относительные пороги селекции по уровню и длительности с вероятностью ложной тревоги на интервале ожидания сигнала. Предложена методика определения порога селекции по длительности для снижения порога селекции по уровню до заданной величины. «Emissional» probability model of the detector in stand-by mode proposed by the authors in [1], is intended for estimation of false alarm rate dependence from the value of time-selection threshold. Fluctuation emissions of the noise indicator process are interpreted as rare events correspond to Poisson distribution. Assuming that average rate of false alarms exceeds the correlation interval of indicator process, obtained equation between average number of false alarms of any duration and average number of false alarms exceed the time threshold. Based on known numerical and statistical characteristics of emissions of normal stationary random process obtained equations, relating time and level thresholds with false alarm probability on stand-by mode time interval. Also suggested a method of determining time threshold intended to reduce level threshold.

A Narrative Method for Evaluating Documents Similarity based on Unique Strings

A precision and efficiency model of the similarity computing of texts plays an important key of duplicate documents detection. In this paper, we focus on presenting and evaluating documents similarity based on a new method viaen coding text into unique strings, called Deoxyribo Nucleic Acid (DNA) sequences. Additionally, the proposed method including an algorithm for marking as well as coloring similar paragraphs in the test document compared to other documents available in the data warehouse and developing a system for copy detection are investigated. Experimental results show that this novel approach is highly accurate for areal dataset taken from PAN. The results corroborate the advantages of the novel approach with average of 99%accuracyfor the text similarity detection with a selection threshold of ε=10-12.The results of this study are applied to implement a practical system for evaluating documents similarity at the University of Danang, Vietnam

Low Base Rates and a High IQ Selection Threshold Prevented Terman from Identifying Future Nobelists

Although the accomplishments of the 1,528 subjects of the Genetic Studies of Genius are impressive, they do not represent the pinnacle of human achievement. Since the early 1990s, commentators have criticized the study because two future Nobelists—William Shockley and Luis Alvarez—were among the candidates screened for the study; but they were rejected because their IQ scores were too low. Critics see this as a flaw of Terman’s methodology and/or intelligence testing. This study simulates the Terman’s sampling procedure to estimate the probability that Terman would have selected one or both future Nobelists from a population of 168,000 candidates. Using simulations, we created a model that reflected the reliability of the IQ scores used to select individuals for the Genetic Studies of Genius and the relationship between IQ and Nobelist status. Results showed that it was unlikely for Terman to identify children who would later earn Nobel prizes, mostly because of the low base rate of earning a Nobel and the high minimum IQ needed to be selected for Terman’s study. Changes to the methodology that would have been required to select one or both Nobelists were not practical. Therefore, future Nobelists’ absence from the Genetic Studies of Genius sample is not a fatal flaw of intelligence testing or Terman’s study. Instead, predicting high levels of eminence requires measuring a variety of relevant cognitive and non-cognitive variables. A preprint version of this paper is available at https://psyarxiv.com/g4x6r/. Simulation code and results and reliability generalization information are available at https://osf.io/3xfe8/.